| /* ---------------------------------------------------------------------*//** |
| * |
| * Licensed to the Apache Software Foundation (ASF) under one |
| * or more contributor license agreements. See the NOTICE file |
| * distributed with this work for additional information |
| * regarding copyright ownership. The ASF licenses this file |
| * to you under the Apache License, Version 2.0 (the |
| * "License"); you may not use this file except in compliance |
| * with the License. You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, |
| * software distributed under the License is distributed on an |
| * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| * KIND, either express or implied. See the License for the |
| * specific language governing permissions and limitations |
| * under the License. |
| * |
| *//* ---------------------------------------------------------------------*/ |
| |
| m4_include(`SQLCommon.m4') |
| |
| \i m4_regexp(MODULE_PATHNAME, |
| `\(.*\)libmadlib\.so', |
| `\1../../modules/deep_learning/test/madlib_keras_iris.setup.sql_in' |
| ) |
| |
| \i m4_regexp(MODULE_PATHNAME, |
| `\(.*\)libmadlib\.so', |
| `\1../../modules/deep_learning/test/madlib_keras_custom_function.setup.sql_in' |
| ) |
| |
| m4_changequote(`<!', `!>') |
| m4_ifdef(<!__POSTGRESQL__!>, <!!>, <! |
| -- Multiple models End-to-End test |
| -- Prepare model selection table with four rows |
| DROP TABLE IF EXISTS pg_temp.mst_table, pg_temp.mst_table_summary; |
| SELECT load_model_selection_table( |
| 'iris_model_arch', |
| 'pg_temp.mst_table', |
| ARRAY[1], |
| ARRAY[ |
| $$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)', metrics=['accuracy']$$, |
| $$loss='categorical_crossentropy', optimizer='Adam(lr=0.001)', metrics=['accuracy']$$, |
| $$loss='categorical_crossentropy', optimizer='Adam(lr=0.0001)', metrics=['accuracy']$$ |
| ], |
| ARRAY[ |
| $$batch_size=16, epochs=1$$ |
| ] |
| ); |
| |
| CREATE SCHEMA __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__; |
| DROP TABLE if exists __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model, |
| __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model_summary, |
| __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model_info; |
| SELECT madlib_keras_fit_multiple_model( |
| 'iris_data_packed', |
| '__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model', |
| 'pg_temp.mst_table', |
| 3, |
| FALSE |
| ); |
| |
| SELECT assert( |
| model_arch_table = 'iris_model_arch' AND |
| validation_table is NULL AND |
| model_info = '__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model_info' AND |
| source_table = 'iris_data_packed' AND |
| model = '__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model' AND |
| dependent_varname = 'class_text' AND |
| independent_varname = 'attributes' AND |
| madlib_version is NOT NULL AND |
| num_iterations = 3 AND |
| start_training_time < now() AND |
| end_training_time < now() AND |
| num_classes = 3 AND |
| class_values = '{Iris-setosa,Iris-versicolor,Iris-virginica}' AND |
| dependent_vartype LIKE '%char%' AND |
| normalizing_const = 1, |
| 'Keras Fit Multiple Output Summary Validation failed. Actual:' || __to_char(summary)) |
| FROM (SELECT * FROM __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model_summary) summary; |
| |
| -- Run Predict |
| DROP TABLE IF EXISTS pg_temp.iris_predict; |
| SELECT madlib_keras_predict( |
| '__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model', |
| 'iris_data', |
| 'id', |
| 'attributes', |
| 'pg_temp.iris_predict', |
| 'prob', |
| NULL, |
| 1); |
| |
| -- Run Evaluate |
| DROP TABLE IF EXISTS pg_temp.evaluate_out; |
| SELECT madlib_keras_evaluate( |
| '__MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__.iris_multiple_model', |
| 'iris_data_val', |
| 'pg_temp.evaluate_out', |
| NULL, |
| 1); |
| |
| SELECT assert(loss >= 0 AND |
| metric >= 0 AND |
| metrics_type = '{accuracy}', 'Evaluate output validation failed. Actual:' || __to_char(evaluate_out)) |
| FROM pg_temp.evaluate_out; |
| |
| -- Test for one-hot encoded user input data |
| DROP TABLE if exists iris_multiple_model, iris_multiple_model_summary, iris_multiple_model_info; |
| SELECT madlib_keras_fit_multiple_model( |
| 'iris_data_one_hot_encoded_packed', |
| 'iris_multiple_model', |
| 'pg_temp.mst_table', |
| 3, |
| FALSE |
| ); |
| |
| SELECT CASE WHEN is_ver_greater_than_gp_640_or_pg_11() is TRUE THEN assert_guc_value('plan_cache_mode', 'auto') END; |
| |
| SELECT assert( |
| model_arch_table = 'iris_model_arch' AND |
| validation_table is NULL AND |
| model_info = 'iris_multiple_model_info' AND |
| source_table = 'iris_data_one_hot_encoded_packed' AND |
| model = 'iris_multiple_model' AND |
| dependent_varname = 'class_one_hot_encoded' AND |
| independent_varname = 'attributes' AND |
| madlib_version is NOT NULL AND |
| num_iterations = 3 AND |
| start_training_time < now() AND |
| end_training_time < now() AND |
| dependent_vartype = 'integer[]' AND |
| num_classes = NULL AND |
| class_values = NULL AND |
| normalizing_const = 1, |
| 'Keras Fit Multiple Output Summary Validation failed when user passes in 1-hot encoded label vector. Actual:' || __to_char(summary)) |
| FROM (SELECT * FROM iris_multiple_model_summary) summary; |
| |
| -- Run Predict |
| DROP TABLE IF EXISTS iris_predict; |
| SELECT madlib_keras_predict( |
| 'iris_multiple_model', |
| 'iris_data_one_hot_encoded', |
| 'id', |
| 'attributes', |
| 'iris_predict', |
| 'prob', |
| NULL, |
| 1); |
| SELECT CASE WHEN is_ver_greater_than_gp_640_or_pg_11() is TRUE THEN assert_guc_value('plan_cache_mode', 'auto') END; |
| |
| -- Run Evaluate |
| DROP TABLE IF EXISTS evaluate_out; |
| SELECT madlib_keras_evaluate( |
| 'iris_multiple_model', |
| 'iris_data_one_hot_encoded_val', |
| 'evaluate_out', |
| NULL, |
| 1); |
| SELECT CASE WHEN is_ver_greater_than_gp_640_or_pg_11() is TRUE THEN assert_guc_value('plan_cache_mode', 'auto') END; |
| |
| SELECT assert(loss >= 0 AND |
| metric >= 0 AND |
| metrics_type = '{accuracy}', 'Evaluate output validation failed. Actual:' || __to_char(evaluate_out)) |
| FROM evaluate_out; |
| |
| -- TEST custom loss function and |
| |
| DROP TABLE IF EXISTS test_custom_function_table; |
| SELECT load_custom_function('test_custom_function_table', custom_function_zero_object(), 'test_custom_fn', 'returns test_custom_fn'); |
| |
| -- Prepare model selection table with four rows |
| DROP TABLE IF EXISTS mst_object_table, mst_object_table_summary; |
| SELECT load_top_k_accuracy_function('test_custom_function_table', 4); |
| SELECT load_model_selection_table( |
| 'iris_model_arch', |
| 'mst_object_table', |
| ARRAY[1], |
| ARRAY[ |
| $$loss='categorical_crossentropy', optimizer='Adam(lr=0.01)', metrics=['accuracy']$$, |
| $$loss='test_custom_fn', optimizer='Adam(lr=0.001)', metrics=['top_4_accuracy']$$ |
| ], |
| ARRAY[ |
| $$batch_size=16, epochs=1$$ |
| ], |
| 'test_custom_function_table' |
| ); |
| |
| DROP TABLE if exists iris_multiple_model_custom_fn, iris_multiple_model_custom_fn_summary, iris_multiple_model_custom_fn_info; |
| SELECT madlib_keras_fit_multiple_model( |
| 'iris_data_packed', |
| 'iris_multiple_model_custom_fn', |
| 'mst_object_table', |
| 3, |
| FALSE, |
| 'iris_data_one_hot_encoded_packed', |
| 1 |
| ); |
| |
| SELECT assert( |
| model_arch_table = 'iris_model_arch' AND |
| validation_table = 'iris_data_one_hot_encoded_packed' AND |
| model_info = 'iris_multiple_model_custom_fn_info' AND |
| source_table = 'iris_data_packed' AND |
| model = 'iris_multiple_model_custom_fn' AND |
| dependent_varname = 'class_text' AND |
| independent_varname = 'attributes' AND |
| madlib_version is NOT NULL AND |
| num_iterations = 3 AND |
| start_training_time < now() AND |
| end_training_time < now() AND |
| num_classes = 3 AND |
| class_values = '{Iris-setosa,Iris-versicolor,Iris-virginica}' AND |
| dependent_vartype LIKE '%char%' AND |
| normalizing_const = 1, |
| 'Keras Fit Multiple Output Summary Validation failed. Actual:' || __to_char(summary)) |
| FROM (SELECT * FROM iris_multiple_model_custom_fn_summary) summary; |
| |
| SELECT assert( |
| model_type = 'madlib_keras' AND |
| model_size > 0 AND |
| fit_params = $MAD$batch_size=16, epochs=1$MAD$::text AND |
| metrics_type = '{top_4_accuracy}' AND |
| training_metrics_final >= 0 AND |
| training_loss_final = 0 AND |
| training_loss = '{0,0,0}' AND |
| array_upper(training_metrics, 1) = 3 AND |
| array_upper(training_loss, 1) = 3 AND |
| validation_metrics_final >= 0 AND |
| validation_loss_final = 0 AND |
| array_upper(validation_metrics, 1) = 3 AND |
| array_upper(validation_loss, 1) = 3 AND |
| array_upper(metrics_elapsed_time, 1) = 3, |
| 'Keras Fit Multiple Output Info Validation failed. Actual:' || __to_char(info)) |
| FROM (SELECT * FROM iris_multiple_model_custom_fn_info where compile_params like '%test_custom_fn%') info; |
| |
| -- Run Predict |
| DROP TABLE IF EXISTS iris_predict; |
| SELECT madlib_keras_predict( |
| 'iris_multiple_model_custom_fn', |
| 'iris_data', |
| 'id', |
| 'attributes', |
| 'pg_temp.iris_predict', |
| 'prob', |
| NULL, |
| 1); |
| |
| -- Run Evaluate |
| DROP TABLE IF EXISTS evaluate_out; |
| SELECT madlib_keras_evaluate( |
| 'iris_multiple_model_custom_fn', |
| 'iris_data_val', |
| 'evaluate_out', |
| NULL, |
| 2); |
| |
| SELECT assert(loss = 0 AND |
| metric >= 0 AND |
| metrics_type = '{top_4_accuracy}' AND |
| loss_type = 'test_custom_fn', 'Evaluate output validation failed. Actual:' || __to_char(evaluate_out)) |
| FROM evaluate_out; |
| |
| |
| DROP SCHEMA __MADLIB__DEEP_LEARNING_SCHEMA__MADLIB__ CASCADE; |
| !>) |